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Episode 3: We Let AI Do More. Yet We Still Check.

AI can take on more work when the goal is clear and the result can be checked. Show graphics and a live website test illustrate how experience guides what to delegate and what to review.

Neural Gains Weekly · Podcast edition

Santosh Savel and Brandon Ratay beside the words AI Doesn’t Own the Outcome, Explore AI Out Loud Episode 3.

Hey everyone!

Episode 3 of Explore AI Out Loud is out today. Brandon and I go behind the scenes of building the show with AI and explore a question that keeps coming up as we give it more work: how do we know the result is ready to use?

Building the show’s visual identity gave us a practical example. We needed graphics that felt consistent across platforms, each with different sizes and layouts. AI helped us research the requirements and develop the designs. Brandon’s marketing and design experience helped him guide that work and see what still needed attention.

He also wanted files he could open and refine in Photoshop. That gave him control over the final details while AI handled more of the work involved in getting there.

I saw something similar when I let an AI agent run a live test of my website. I gave it the goal and let it carry out the work, then checked that the site behaved as expected and the temporary changes had been undone. Knowing my own website helped me understand what to look for.

These experiences are making me more comfortable giving AI responsibility. I can let it do more when I understand the task and have a way to check the result. When I’m learning something unfamiliar, smaller steps help me follow what changed and build that confidence.

Try this with a task you know well enough to judge. Describe what a useful result would look like, let AI help you get there, then check the details that matter.

We also discuss AI adoption at work and the responsibility people carry when AI contributes to consequential decisions. Throughout the episode, we explore how to do more with these tools while understanding the work well enough to stand behind it.

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Find practical ways to use AI in your work and everyday life. Learn from real experiments, including the mistakes, so you can spend less time guessing and more time trying something useful. New episodes every other Tuesday.

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Santosh Savel

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